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CUTTING THROUGH THE NOISE
that are more encompassing onto that to give the EWOs a wider
range of what they can identify, said Monto.
While the EWOs would remain as the lead for identifying signals
of interest and analyzing their impact, the use of artificial intel-
ligence and machine learning could help them quickly and
accurately detect patterns, identify signals of significance, filter
out unwanted signal noise and paint a picture of the electromag-
netic spectrum.
THE CHALLENGE
RCO’s Army Signal Classification Challenge began April 30 and
closed Aug. 13. After opening registration online, competitors
were given access to the training data set, consisting of more
than 4.3 million instances across 24 different modulations, which
included a noise class. (The noise class represents “white” noise to
replicate the real-life environment that signals would be detected
in, rather than a pristine lab environment.) The effort sought
solutions that could perform “blind” signal classification quickly
and accurately. Blind signal classification requires little to no
prior knowledge about the signal being detected in that specific
instance. Instead, the solution would automatically classify the
modulation, or change of a radio frequency waveform, as a first
step toward signal classification.
The challenge gave participants 90 days to develop their models
and to work with the training data sets. That was followed by
two test data sets of varying complexity that were the basis for
judging submissions. The first data set was released 67 days after
the challenge launch, with a solution submission window of 15
days. A second, more complex test data set was released 84 days
after the challenge launch, with a shorter submission window of
only seven days.
Participants’ scores were based on a combined weighted score for
both test data sets. Competitors could see how well they were
performing against their peers through a participant leader board
that showed scores in real time.
For first-place winners Team Platypus—which participated in the
Defense Advanced Research Projects Agency’s Software Defined
Radio Hackfest 2017 and whose name references platypuses’ abil-
ity to detect electrical fields with their bills—the challenge lined
up perfectly with its core research in artificial intelligence and
advanced signal processing.
“We really enjoyed the challenge process, which included the hard
problem curation, providing training data and a specific scoring
algorithm,” Vila said. “To do this with the highest level of confi-
dence, we had to use a multipronged approach. We built statistics
and metrics inspired by communication principles, and we also
developed deep learning classifiers that work directly on the raw
data. We ended up using several state-of-the-art AI techniques
to achieve the winning submi s si o n .”
Their technology includes an algorithm trained to identify
what kind of signal is present in the midst of a congested radio
frequency environment, much like Soldiers would find in an
urban core or battlefield where both friendly and enemy radio
communications are being detected.
CONCLUSION
By structuring this effort as a challenge and not going through
the traditional RFI process, RCO proved it could take an indus-
try model and move fast. For its efforts, it is substantially closer
to identifying a potential solution that could be applied to
battlefield electronic warfare capabilities in the very near future.
The challenge also showed that RCO could harness the prom-
ise of artificial intelligence and machine learning by applying
it to a specific problem. The amount of interest and quality of
performance, including from nontraditional organizations, was
remarkable.
Now, RCO is quickly moving forward to the next step, with two
possible options. First, RCO could initiate a second, more intense
challenge and open it up to only the top performers in the first
challenge. Or, RCO could begin to immediately move the algo-
rithms into the hands of Soldiers through software enhancements
to their existing electronic warfare equipment. This would enable
the Soldiers to give immediate feedback and enable the Army to
incrementa lly build capabi l it y.
Over the next several months, RCO will begin to advance what
was learned from the challenge, potentially prototyping the lead-
ing artificial intelligence and machine learning algorithms into
Army electronic warfare systems.
For more information on the Army RCO, go to http://
rapidcapabilitiesoffice.army.mil/.
NANCY JONES-BONBREST is a public communications specialist
for the Army RCO and has written extensively about Army modern-
ization and acquisition for several years, including multiple
training and testing events. She holds a B.S . in journalism
from the University of Maryland, College Park.
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74
Army AL&T Magazine
January-March 2019